Machine Learning (ML) is a subset of AI that enables systems to learn from data and improve over time without explicit programming. ML utilizes algorithms to detect patterns, make predictions, and automate decision-making across various domains. Implemented on large datasets, it supports scalability and performance optimization. ML applications range from recommendation systems to advanced analytics in global industries.

• A fundamental understanding of statistics, linear algebra, and algorithms is helpful. 

• Familiarity with basic computer operations and data handling will enhance learning in Machine Learning (ML).

Module 1: Introduction to Machine Learning

• What is Machine Learning?
  • Definition and history.
  • Applications of ML in various industries (Healthcare, Finance, Retail, etc.).
• Types of Machine Learning
  • Supervised Learning.
  • Unsupervised Learning.
  • Reinforcement Learning.
  • Semi-Supervised Learning.
• Key ML Terminologies
  • Features, labels, training data, and test data.
  • Overfitting and underfitting.
  • Bias-variance tradeoff.

Module 2: Mathematics for Machine Learning

• Linear Algebra
  • Matrices, vectors, and matrix operations.
  • Eigenvalues, eigenvectors, and matrix decomposition.
• Probability and Statistics
  • Basics of probability (Bayes’ theorem, probability distributions).
  • Descriptive statistics (mean, median, variance).
• Calculus
  • Derivatives, gradients, and optimization.
  • Gradient descent and its variants (Stochastic Gradient Descent, Mini-batch Gradient Descent).
• Multivariate Analysis
  • Correlation and covariance.
  • Principal Component Analysis (PCA).

Module 3: Data Preprocessing

• Understanding Data
  • Data collection and cleaning.
  • Handling missing values, outliers, and anomalies.
• Feature Engineering
  • Feature scaling (Normalization, Standardization).
  • Encoding categorical data (One-hot encoding, Label encoding).
  • Feature selection and dimensionality reduction.
• Exploratory Data Analysis (EDA)
  • Visualization techniques (scatter plots, histograms, correlation matrices).
  • Identifying patterns and trends.

Module 4: Supervised Learning

• Regression
  • Linear regression and logistic regression.
  • Polynomial regression and Ridge/Lasso regression.
• Classification
  • Decision Trees and Random Forests.
  • Support Vector Machines (SVM).
  • k-Nearest Neighbors (k-NN).
• Evaluation Metrics
  • Mean Squared Error (MSE), R² for regression.
  • Confusion matrix, Precision, Recall, F1-Score for classification.

Module 5: Unsupervised Learning

• Clustering
  • k-Means clustering.
  • Hierarchical clustering.
  • Density-Based Spatial Clustering (DBSCAN).
• Dimensionality Reduction
  • Principal Component Analysis (PCA).
  • t-Distributed Stochastic Neighbor Embedding (t-SNE).
• Applications of Unsupervised Learning
  • Market segmentation.
  • Anomaly detection.

Module 6: Advanced Machine Learning

• Ensemble Methods
  • Bagging (Bootstrap Aggregating), Boosting (AdaBoost, Gradient Boosting).
  • XGBoost, LightGBM, CatBoost.
• Reinforcement Learning
  • Markov Decision Process (MDP).
  • Q-learning and Deep Q-Networks (DQN).
• Time Series Analysis
  • Moving averages, ARIMA, and Prophet models.
  • Applications in stock price prediction and demand forecasting.

Module 7: Neural Networks and Deep Learning Basics

• Introduction to Neural Networks
  • Structure and working of an artificial neuron.
  • Activation functions (Sigmoid, ReLU, Tanh, etc.).
• Deep Learning
  • Introduction to Convolutional Neural Networks (CNN).
  • Recurrent Neural Networks (RNN) for sequence modeling.

Module 8: Machine Learning Tools and Frameworks

• Programming Languages
  • Python for ML: Libraries like NumPy, pandas, matplotlib, scikit-learn.
  • R for statistical modeling and machine learning.
• ML Frameworks
  • TensorFlow and PyTorch.
  • Keras for rapid prototyping.
• ML Platforms
  • Google AI Platform, AWS SageMaker, Azure ML Studio.

Module 9: Deployment and Model Optimization

• Model Deployment
  • Exporting models (ONNX, SavedModel, etc.).
  • Deployment on cloud platforms (AWS, GCP, Azure).
  • Model serving using REST APIs (Flask, FastAPI).
• Model Optimization
  • Hyperparameter tuning (Grid Search, Random Search).
  • Cross-validation techniques (k-fold, Leave-One-Out).
  • Model interpretability (SHAP, LIME).

Module 10: Ethics and Responsible ML

• Bias and Fairness
  • Identifying and mitigating algorithmic bias.
  • Ensuring fairness in decision-making.
• Privacy and Security
  • Data anonymization techniques.
  • GDPR and data handling policies.
• AI for Good
  • Applications of ML for social impact.

Module 11: Real-World Applications of Machine Learning

• Healthcare
  • Predictive analytics, diagnostics, and personalized medicine.
• Finance
  • Fraud detection, credit scoring, and algorithmic trading.
• Retail
  • Recommendation systems, demand forecasting, and inventory management.
• Others
  • Autonomous vehicles, gaming, and natural language processing.

Module 12: Capstone Project

• End-to-End ML Project
  • Problem identification and dataset acquisition.
  • Data preprocessing and model training.
  • Evaluation and deployment.
• Documentation and Presentation
  • Create a comprehensive report of the project.
  • Present the solution to a panel or audience.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support